Abstract: Contact dynamics hold immense amounts of information that can improve a robot’s ability to characterize and learn about objects in their environment through interactions. However, collecting information-rich contact data is challenging due to its inherent sparsity and non-smooth nature, requiring an active approach to maximize the utility of contacts for learning. In this work, we investigate an optimal experimental design approach to synthesize robot behaviors that produce contact-rich data for learning. Our approach derives a contact-aware Fisher information measure that characterizes information-rich contact behaviors that improve learning. We observe emergent robot behaviors that are able to excite contact interactions that efficiently learns object parameters across a range of examples. Last, we demonstrate the utility of contact-awareness for learning contact-seeking behavior on several robotic experiments.